{"doi":"10.1016/j.jneumeth.2024.110203","title":"Brain-computer interfaces inspired spiking neural network model for depression stage identification","abstract":null,"journal":"Journal of Neuroscience Methods","year":2024,"id":677760,"datarank":0.37273599746820013,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.0,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":569786,"name":"Monika Anand","orcid":"0000-0001-8634-4952","position":1,"is_corresponding":false},{"id":1770868,"name":"Mahmood Alsaadi","orcid":null,"position":2,"is_corresponding":false},{"id":1264604,"name":"Ashit Kumar Dutta","orcid":"0000-0002-0748-9174","position":3,"is_corresponding":false},{"id":1770869,"name":"Roma Fayaz","orcid":null,"position":4,"is_corresponding":false},{"id":1770872,"name":"Sojomon Mathew","orcid":null,"position":5,"is_corresponding":false},{"id":1770874,"name":"Mousmi Ajay Chaurasia","orcid":null,"position":6,"is_corresponding":false},{"id":1770877,"name":"Sunila","orcid":null,"position":7,"is_corresponding":false},{"id":1770879,"name":"Manisha Bhende","orcid":null,"position":8,"is_corresponding":false},{"id":1770865,"name":"M. Angelin Ponrani","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Brain-computer interfaces inspired spiking neural network model for depression stage identification","abstract":"<h4>Background</h4>Depression is a global mental disorder, and traditional diagnostic methods mainly rely on scales and subjective evaluations by doctors, which cannot effectively identify symptoms and even carry the risk of misdiagnosis. Brain-Computer Interfaces inspired deep learning-assisted diagnosis based on physiological signals holds promise for improving traditional methods lacking physiological basis and leads next generation neuro-technologies. However, traditional deep learning methods rely on immense computational power and mostly involve end-to-end network learning. These learning methods also lack physiological interpretability, limiting their clinical application in assisted diagnosis.<h4>Methodology</h4>A brain-like learning model for diagnosing depression using electroencephalogram (EEG) is proposed. The study collects EEG data using 128-channel electrodes, producing a 128×128 brain adjacency matrix. Given the assumption of undirected connectivity, the upper half of the 128×128 matrix is chosen in order to minimise the input parameter size, producing 8,128-dimensional data. After eliminating 28 components derived from irrelevant or reference electrodes, a 90×90 matrix is produced, which can be used as an input for a single-channel brain-computer interface image.<h4>Result</h4>At the functional level, a spiking neural network is constructed to classify individuals with depression and healthy individuals, achieving an accuracy exceeding 97.5 %.<h4>Comparison with existing methods</h4>Compared to deep convolutional methods, the spiking method reduces energy consumption.<h4>Conclusion</h4>At the structural level, complex networks are utilized to establish spatial topology of brain connections and analyse their graph features, identifying potential abnormal brain functional connections in individuals with depression.","is_dataset_classified":null,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38880343","pmcid":null,"openalex_id":"https://openalex.org/W4399709249","authors":[],"funders":[{"funder_name":"AlMaarefa University","grant_id":"","title":null}],"total_grants":1,"fwci":2.6746,"citation_percentile":0.9012649,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":6},{"year":2026,"count":4}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-004","oa_locations":[{"url":"https://api.elsevier.com/content/article/PII:S0165027024001481?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0165027024001481?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.jneumeth.2024.110203","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38880343","host_type":"repository"}],"fields_of_study":["EEG and Brain-Computer Interfaces","Functional Brain Connectivity Studies","Emotion and Mood Recognition","Humans","Brain-Computer Interfaces","Electroencephalography","Neural Networks, Computer","Depression","Brain","Deep Learning","Models, Neurological","Adult","Action Potentials"],"mesh_terms":["Deep Learning","Action Potentials","Adult","Brain","Depression","Electroencephalography","Humans","Models, Neurological","Neural Networks, Computer","Brain-Computer Interfaces"],"keywords":["Interpretability","Limiting","Artificial intelligence","Computer science","Deep learning","Identification (biology)","Machine learning","Artificial neural network","Depression (economics)","Brain–computer interface","Neuroscience","Psychology","Electroencephalography","Depression","Brain-computer interface","Eeg Signals","Next Generation Neuro-technologies","Pulse Neural Network"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-17T04:31:17.124954Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}